Feature-based Restaurant Customer Reviews Process Model using Data Mining

2018 ◽  
Author(s):  
Anish Kumar Varudharajulu ◽  
Yongsheng Ma
Keyword(s):  
Data Mining ◽  
Process Model ◽  
Customer Reviews ◽  
Feature Based ◽  
Using Data

Feature Based Opinion Mining

2019 ◽  
pp. 20-39
Author(s):  
Mridula Batra ◽  
Vishaw Jyoti
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Abstraction ◽  
Correct Decision ◽  
Customer Reviews ◽  
Feature Based ◽  
Source Of Information ◽  
Judgment Data

Opinion mining is the estimated learning of user's beliefs, evaluation and sentiments about units, actions and its features. This method has several features matched with data mining techniques, language processing methods and feature oriented data abstraction. This seems to be extremely difficult to mine opinions from analysis those exist in common human used language. Views are very essentials when one desires to construct a judgment. Data abstraction is an important characteristic for decision making applicable to individuals and organization of different nature. While selecting and purchasing a particular product, it is always beneficial for an individual to collect other views for correct decision making. One association wants to conduct surveys and gather opinions to develop their product excellence. Internet as a source of information, having a number of websites available with the customer reviews as a number of products, it is easy to extract the features from these opinions, sentiments and view, is a task comes under feature-based opinion mining.

An Integrated Approach of Proposed Pruning Based Feature Selection Technique (PBFST) for Phishing E-mail Detection

2021 ◽  
Vol 14 ◽  
Author(s):  
Hari Shanker Hota ◽  
Dinesh Sharma ◽  
Akhilesh Shrivas
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Secure Communication ◽  
Optimization Technique ◽  
Integrated Approach ◽  
Feature Selection Technique ◽  
Data Set ◽  
Selection Technique ◽  
Feature Based ◽  
Using Data

Introduction: Entire world is shifting towards electronic communication through Email for fast and secure communication. Millions of people, including organization, government, and others, are using Email services. This growing number of Email users are facing problems; therefore, detecting phishing Email is a challenging task, especially for non-IT users. Automatic detection of phishing Email is essential to deploy along with Email software. Various authors have worked in the field of phishing Email classification with different feature selection and optimization technique for better performance. Objective: This paper attempts to build a model for the detection of phishing Email using data mining techniques. This paper's significant contribution is to develop and apply Feature Selection Technique (FST) to reduce features from the phishing Email benchmark data set. Methods: The proposed Pruning Based Feature Selection Technique (PBFST) is used to determine the rank of feature based on the level of the tree where feature exists. The proposed algorithm is integrated with already developed Bucket Based Feature Selection Technique (BBFST). BBFST is used as an internal part to rank features in a particular level of the tree. Results : Experimental work was done with open source WEKA data mining software using a 10-fold cross-validation technique. The proposed FST was compared with other ranking based FSTs to check the performance of C4.5 classifier with Phishing Email data set. Conclusion: The proposed FST reduces 33 features out of 47 features which exist in phishing Email data set and C4.5 algorithm produces remarkable accuracy of 99.06% with only 11 features and found to be better than other existing FST.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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